Veng-Pedersen P, Gobburu J V, Meyer M C, Straughn A B
College of Pharmacy, University of Iowa, Iowa City, IA 52242, USA.
Biopharm Drug Dispos. 2000 Jan;21(1):1-6. doi: 10.1002/1099-081x(200001)21:1<1::aid-bdd207>3.0.co;2-d.
A method is presented for prediction of the systemic drug concentration profile from in vitro release/dissolution data for a drug formulation. The method is demonstrated using four different tablet formulations containing 200 mg carbamazepine (CZM), each administered in a four way cross-over manner to 20 human subjects, with 15 blood samples drawn to determine the resulting concentration profile. Amount versus time dissolution data were obtained by a 75 rpm paddle method for each formulation. Differentiation, with respect to time, of a monotonic quadratic spline fitted to the dissolution data provided the dissolution rate curve. The dissolution curve was through time and magnitude scaling mapped into a drug concentration curve via a convolution by a single exponential, and the estimated unit impulse response function. The method was tested by cross-validation, where the in vivo concentration profiles for each formulation were predicted based on correlation parameters determined from in vivo-in vitro data from the remaining three formulations. The mean prediction error (MPE), defined as the mean value of 100% x(observed-predicted)/observed was calculated for all 240 cross-validation predictions. The mean values of MPE were in the range of 10-36% (average 22%) with standard deviations (S.D.s) in the range of 9-33% (average 13%), indicating a good prediction performance of the proposed in vivo-in vitro correlation (IVIVC) method.
本文提出了一种根据药物制剂的体外释放/溶出数据预测全身药物浓度曲线的方法。使用四种含有200mg卡马西平(CZM)的不同片剂制剂对该方法进行了验证,每种制剂均以四交叉方式给予20名人类受试者,并采集15份血样以确定所得的浓度曲线。通过75rpm桨法获得每种制剂的量随时间的溶出数据。对拟合到溶出数据的单调二次样条进行时间微分,得到溶出速率曲线。通过单次指数卷积和估计的单位脉冲响应函数,将溶出曲线通过时间和幅度缩放映射为药物浓度曲线。通过交叉验证对该方法进行了测试,其中基于从其余三种制剂的体内-体外数据确定的相关参数预测每种制剂的体内浓度曲线。对所有240次交叉验证预测计算平均预测误差(MPE),定义为100%×(观察值-预测值)/观察值的平均值。MPE的平均值在10%-36%范围内(平均22%),标准差(S.D.s)在9%-33%范围内(平均13%),表明所提出的体内-体外相关性(IVIVC)方法具有良好的预测性能。